Research of Consumption Behavior Prediction Based on Improved DNN

Abstract

In terms of the low accuracy and unsatisfactory effect of traditional prediction models for consumption behavior, in the study of deep learning DNN model, a consumption behavior prediction model based on rDNN model is proposed. By choosing the appropriate function as the activation function of the model, the random sampling method is used to select negative samples of consumer behavior data to determine the N/P ratio, which improves the DNN model. Based on the improved DNN model, a consumer behavior prediction model based on the rDNN model is constructed. The results show that when the tanh function is used as the activation function and the ratio of N/P is 3, the rDNN model has the best prediction effect on consumption behavior, with AUC value of 0.8422 and the fastest operation efficiency of 434.36 s. Compared with traditional prediction models, DNN, and KmDNN deep learning models, the proposed model has more reliable prediction results and can be used to predict actual consumption behavior.